MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images.
MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images.
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DOI：
10.1016/j.bspc.2020.102257
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发表时间：
2021-03
影响因子：
5.1
通讯作者：
Canayaz M
中科院分区：
文献类型：
--
作者：
Canayaz M
研究显示了在x射线图像中使用图像对比度增强技术后，特征提取对分类结果的影响。在元启发式算法的帮助下，对x射线图像上选择的少量特征进行分类性能评估。它提供了一种有助于根据x射线图像诊断covid-19的方法。COVID-19是一种在世界各地引起肺部症状并导致死亡的疾病。目前正在进行诊断和治疗这种疾病的研究，这种疾病被定义为大流行。这种疾病的早期诊断对人的生命至关重要。随着基于深度学习的诊断研究的发展，这一进程正在迅速发展。因此，为了在这一领域做出贡献，我们的研究提出了一种基于深度学习的方法，可以用于疾病的早期诊断。在该方法中，创建了一个由covid - 19、正常和肺炎3类肺部x射线图像组成的数据集，每类包含364张图像。利用图像对比度增强算法对准备好的数据集进行预处理，得到新的数据集。利用AlexNet、VGG19、GoogleNet和ResNet等深度学习模型完成了该数据集的特征提取。对于最佳潜在特征的选择，采用了二元粒子群优化和二元灰狼优化两种元启发式算法。将增强数据集的特征选择得到的特征结合起来，使用支持向量机进行分类。该方法的总体准确率为99.38%。两种不同的元启发式算法验证结果证明，我们提出的方法可以帮助专家进行COVID-19诊断研究。
The study shows the effect of feature extraction on classification results after using the image contrast enhancement technique in X-ray images. Assessment of classification performances with a small number of features selected over X-ray images with the help of meta-heuristic algorithms. It offers an approach that helps the diagnosis of covid-19 on X-ray images. COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.
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